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Records with Keyword: Fault Detection
Detection and Diagnosis of Ring Formation in Rotary Lime Kilns
Lee D Rippon, Barry Hirtz, Carl Sheehan, Travis Reinheimer, Cilius van der Merwe, Philip Loewen, Bhushan Gopaluni
October 21, 2021 (v1)
Keywords: data visualization, Fault Detection, fault diagnosis, process monitoring, pulp and paper, rotary kiln
Rotary lime kilns are large-scale, energy-intensive unit operations that serve critical functions in a variety of industrial processes including cement production, pyrometallurgy, and kraft pulping. As massive expensive vessels that operate at high temperatures it is imperative from economic, environmental, and safety perspectives to optimize preventative maintenance and production efficiency. To achieve these objectives rotary kilns are increasingly outfitted with more sophisticated sensing technology that can provide additional operating insights. Although increasingly intricate data is collected from industrial operations the extent to which value is extracted from this data is often far from optimal. Our research aims to improve this situation by developing data analytics methods that leverage advanced industrial sensor data to address outstanding process faults. Specifically, this research investigates the use of infrared thermal cameras to detect and diagnose ring formation in r... [more]
Motor Fault Detection Using Wavelet Transform and Improved PSO-BP Neural Network
Chun-Yao Lee, Yi-Hsin Cheng
April 30, 2021 (v1)
Keywords: back propagation neural network, Fault Detection, induction motors, particle swarm optimization wavelet transform
This paper proposes a motor fault detection method based on wavelet transform (WT) and improved PSO-BP neural network which is combined with improved particle swarm optimization (PSO) and a back propagation (BP) neural network with linearly increasing inertia weight. First, this research used WT to analyze the current signals of the healthy motor, bearing damage motor, stator winding inter-turn short circuit motor, and broken rotor bar motor. Second, features after completing the signal analysis were extracted, and three types of classifiers were used to classify. The results show that the improved PSO-BP neural network can effectively detect the cause of failure. In addition, in order to simulate the actual operating environment of the motor, this study added white noise with signal noise ratios of 30 dB, 25 dB, and 20 dB to verify that this model has a better anti-noise ability.
A Review on Fault Detection and Process Diagnostics in Industrial Processes
You-Jin Park, Shu-Kai S. Fan, Chia-Yu Hsu
March 14, 2021 (v1)
Keywords: data-driven methods, Fault Detection, fault diagnosis, fault prognosis, hybrid method, industrial process, knowledge-based methods, model-based methods
The main roles of fault detection and diagnosis (FDD) for industrial processes are to make an effective indicator which can identify faulty status of a process and then to take a proper action against a future failure or unfavorable accidents. In order to enhance many process performances (e.g., quality and throughput), FDD has attracted great attention from various industrial sectors. Many traditional FDD techniques have been developed for checking the existence of a trend or pattern in the process or whether a certain process variable behaves normally or not. However, they might fail to produce several hidden characteristics of the process or fail to discover the faults in processes due to underlying process dynamics. In this paper, we present current research and developments of FDD approaches for process monitoring as well as a broad literature review of many useful FDD approaches.
Temporal-Spatial Neighborhood Enhanced Sparse Autoencoder for Nonlinear Dynamic Process Monitoring
Nanxi Li, Hongbo Shi, Bing Song, Yang Tao
February 22, 2021 (v1)
Keywords: Bayesian, dynamic process, Fault Detection, sparse autoencoder, temporal-spatial neighborhood
Data-based process monitoring methods have received tremendous attention in recent years, and modern industrial process data often exhibit dynamic and nonlinear characteristics. Traditional autoencoders, such as stacked denoising autoencoders (SDAEs), have excellent nonlinear feature extraction capabilities, but they ignore the dynamic correlation between sample data. Feature extraction based on manifold learning using spatial or temporal neighbors has been widely used in dynamic process monitoring in recent years, but most of them use linear features and do not take into account the complex nonlinearities of industrial processes. Therefore, a fault detection scheme based on temporal-spatial neighborhood enhanced sparse autoencoder is proposed in this paper. Firstly, it selects the temporal neighborhood and spatial neighborhood of the sample at the current time within the time window with a certain length, the spatial similarity and time serial correlation are used for weighted reconst... [more]
Establish Induction Motor Fault Diagnosis System Based on Feature Selection Approaches with MRA
Chun-Yao Lee, Meng-Syun Wen
February 22, 2021 (v1)
Keywords: artificial neural network, correlation and fitness value-based feature selection, correlation-based feature selection, Fault Detection, feature selection, multiresolution analysis
This paper proposes a feature selection (FS) approach, namely, correlation and fitness value-based feature selection (CFFS). CFFS is an improvement feature selection approach of correlation-based feature selection (CFS) for the common failure cases of the induction motor. CFFS establishes the induction motor fault detection (FD) system with artificial neural network (ANN). This study analyzes the current signal of the induction motor with multiresolution analysis (MRA), extracts the features, and uses feature selection approaches (ReliefF, CFS, and CFFS) to reduce the number of features and maintain the accuracy of the induction motor fault detection system. Finally, the induction motor fault detection system is trained by the feature selection approaches selected features. The best induction motor fault detection system will be established through the comparison of the efficiency of these FS approaches.
Wind Turbine Condition Monitoring Strategy through Multiway PCA and Multivariate Inference
Francesc Pozo, Yolanda Vidal, Óscar Salgado
June 23, 2020 (v1)
Keywords: condition monitoring, Fault Detection, multivariate statistical hypothesis testing, principal component analysis, wind turbine
This article states a condition monitoring strategy for wind turbines using a statistical data-driven modeling approach by means of supervisory control and data acquisition (SCADA) data. Initially, a baseline data-based model is obtained from the healthy wind turbine by means of multiway principal component analysis (MPCA). Then, when the wind turbine is monitorized, new data is acquired and projected into the baseline MPCA model space. The acquired SCADA data are treated as a random process given the random nature of the turbulent wind. The objective is to decide if the multivariate distribution that is obtained from the wind turbine to be analyzed (healthy or not) is related to the baseline one. To achieve this goal, a test for the equality of population means is performed. Finally, the results of the test can determine that the hypothesis is rejected (and the wind turbine is faulty) or that there is no evidence to suggest that the two means are different, so the wind turbine can be... [more]
A Review of Kernel Methods for Feature Extraction in Nonlinear Process Monitoring
Karl Ezra Pilario, Mahmood Shafiee, Yi Cao, Liyun Lao, Shuang-Hua Yang
February 12, 2020 (v1)
Keywords: Fault Detection, fault diagnosis, kernel CCA, kernel CVA, kernel FDA, kernel ICA, kernel PCA, kernel PLS, Machine Learning, Multivariate Statistics
Kernel methods are a class of learning machines for the fast recognition of nonlinear patterns in any data set. In this paper, the applications of kernel methods for feature extraction in industrial process monitoring are systematically reviewed. First, we describe the reasons for using kernel methods and contextualize them among other machine learning tools. Second, by reviewing a total of 230 papers, this work has identified 12 major issues surrounding the use of kernel methods for nonlinear feature extraction. Each issue was discussed as to why they are important and how they were addressed through the years by many researchers. We also present a breakdown of the commonly used kernel functions, parameter selection routes, and case studies. Lastly, this review provides an outlook into the future of kernel-based process monitoring, which can hopefully instigate more advanced yet practical solutions in the process industries.
On Real-Time Fault Detection in Wind Turbines: Sensor Selection Algorithm and Detection Time Reduction Analysis
Francesc Pozo, Yolanda Vidal, Josep M. Serrahima
January 7, 2019 (v1)
Keywords: FAST, Fault Detection, hypothesis test, principal component analysis, sensor selection
In this paper, we address the problem of real-time fault detection in wind turbines. Starting from a data-driven fault detection method, the contribution of this paper is twofold. First, a sensor selection algorithm is proposed with the goal to reduce the computational effort of the fault detection method. Second, an analysis is performed to reduce the data acquisition time needed by the fault detection method, that is, with the goal of reducing the fault detection time. The proposed methods are tested in a benchmark wind turbine where different actuator and sensor failures are simulated. The results demonstrate the performance and effectiveness of the proposed algorithms that dramatically reduce the number of sensors and the fault detection time.
Open Fault Detection and Tolerant Control for a Five Phase Inverter Driving System
Seung-Koo Baek, Hye-Ung Shin, Seong-Yun Kang, Choon-Soo Park, Kyo-Beum Lee
November 27, 2018 (v1)
Keywords: Fault Detection, fault-tolerant control, five-phase induction machine, five-phase induction motor (IM), five-phase inverter
This paper proposes a fault detection and the improved fault-tolerant control for an open fault in the five-phase inverter driving system. The five-phase induction machine has a merit of fault-tolerant control due to its increased number of phases. This paper analyzes an open fault pattern of one switch and proposes an effective fault detection method based upon this analysis. The proposed fault detection method using the analyzed patterns is applied in the power inverter. In addition, when the open fault occurs in the one switch of the induction machine driving system, the proposed fault-tolerant control method is used to operate the induction machine using the remaining healthy phases, after performing the fault detection method. Simulation and experiment results are provided to validate the proposed technique.
Wind Turbine Fault Detection through Principal Component Analysis and Statistical Hypothesis Testing
Francesc Pozo, Yolanda Vidal
October 22, 2018 (v1)
Keywords: FAST (Fatigue, Aerodynamics, Structures and Turbulence), Fault Detection, principal component analysis, statistical hypothesis testing, wind turbine
This paper addresses the problem of online fault detection of an advanced wind turbine benchmark under actuators (pitch and torque) and sensors (pitch angle measurement) faults of different type: fixed value, gain factor, offset and changed dynamics. The fault detection scheme starts by computing the baseline principal component analysis (PCA) model from the healthy or undamaged wind turbine. Subsequently, when the structure is inspected or supervised, new measurements are obtained are projected into the baseline PCA model. When both sets of data—the baseline and the data from the current wind turbine—are compared, a statistical hypothesis testing is used to make a decision on whether or not the wind turbine presents some damage, fault or misbehavior. The effectiveness of the proposed fault-detection scheme is illustrated by numerical simulations on a well-known large offshore wind turbine in the presence of wind turbulence and realistic fault scenarios. The obtained results demonstrat... [more]
Fault Detection for Gas Turbine Hot Components Based on a Convolutional Neural Network
Jiao Liu, Jinfu Liu, Daren Yu, Myeongsu Kang, Weizhong Yan, Zhongqi Wang, Michael G. Pecht
September 21, 2018 (v1)
Keywords: convolutional neural network (CNN), exhaust gas temperature (EGT), Fault Detection, gas turbine, hot component
Gas turbine hot component failures often cause catastrophic consequences. Fault detection can improve the availability and economy of hot components. The exhaust gas temperature (EGT) profile is usually used to monitor the performance of the hot components. The EGT profile is uniform when the hot component is healthy, whereas hot component faults lead to large temperature differences between different EGT values. The EGT profile swirl under different operating and ambient conditions also cause temperature differences. Therefore, the influence of EGT profile swirl on EGT values must be eliminated. To improve the detection sensitivity, this paper develops a fault detection method for hot components based on a convolutional neural network (CNN). This paper demonstrates that a CNN can extract the information between adjacent EGT values and consider the impact of the EGT profile swirl. This paper reveals, in principle, that a CNN is a viable solution for dealing with fault detection for hot... [more]
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